code: def tree_unflatten(cls, aux_data, children):
      del aux_data
      kde = cls.__new__(cls)
      kde._setattr("neff", children[0])
      kde._setattr("dataset", children[1])
      kde._setattr("weights", children[2])
      kde._setattr("covariance", children[3])
      kde._setattr("inv_cov", children[4])
      return kde
  
imports: from dataclasses import dataclass
  from functools import partial
  from typing import Any
  import numpy
  import scipy.stats
  import jax.numpy
  from jax import jit, lax, random, vmap
  from jax._src.numpy.util import check_arraylike, promote_dtypes_inexact, _wraps
  from jax._src.tree_util import register_pytree_node_class
  from jax.scipy import linalg, special
label_desc: Use image node_id 4 for calling the gaussian_kde obj's underlying member method code with example usage: obj.tree_unflatten(cls, aux_data, children) and returns: kde